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Magnetic resonance (MR) images are normally corrupted by Rician noise which may mask the fine details in the image and decreases its resolution. This makes the feature extraction complicated which results in unreliable data analysis. Therefore denoising methods have been applied to improve the image quality. In this study, we propose an optimized single stage principal component analysis (OSPCA) to balance between noise reduction and information preservation. Principal component analysis highlights the data similarities and differences in a better way, which helps in separating noise and edges. Denoising is performed efficiently when the noise and edges are separated. The performance metrics Mean Square Error (MSE), Structural similarity index (SSIM), and Edge Preservation Index (EPI) shows the ability of the proposed method in denoising and preserving information.